Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
BoosTexter: A Boosting-based Systemfor Text Categorization
Machine Learning - Special issue on information retrieval
Knowledge Discovery in Multi-label Phenotype Data
PKDD '01 Proceedings of the 5th European Conference on Principles of Data Mining and Knowledge Discovery
Multi-labelled classification using maximum entropy method
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Multilabel Neural Networks with Applications to Functional Genomics and Text Categorization
IEEE Transactions on Knowledge and Data Engineering
ML-KNN: A lazy learning approach to multi-label learning
Pattern Recognition
Random k-Labelsets: An Ensemble Method for Multilabel Classification
ECML '07 Proceedings of the 18th European conference on Machine Learning
Multi-label Classification Using Ensembles of Pruned Sets
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
MMDT: a multi-valued and multi-labeled decision tree classifier for data mining
Expert Systems with Applications: An International Journal
A multilabel text classification algorithm for labeling risk factors in SEC form 10-K
ACM Transactions on Management Information Systems (TMIS)
Hi-index | 12.05 |
Multi-valued and multi-labeled learning is concerned with samples associated with a set of values both with label and attribute. This paper proposes a new learning framework, which combines multi-valued attribute decomposition with multi-label learning. To deal with multi-valued attribute, we present five methods which differ in strategies with the correlations of multi values. After data transformation, three classic multi-label algorithms are employed for learning. Experimental results demonstrate that most combined methods significantly outperform the existing decision tree based algorithms. Furthermore, exploring the advantages and limitations of each combined method, we find the optimal combination corresponding to different types of datasets.